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1. Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models

   https://doi.org/10.1609/aaai.v38i21.30396  

   Islam, Khairul (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   The widespread use of Artificial Intelligence (AI) has highlighted the importance of understanding AI model behavior. This understanding is crucial for practical decision-making, assessing model reliability, and ensuring trustworthiness. Interpreting time series forecasting models faces unique challenges compared to image and text data. These challenges arise from the temporal dependencies between time steps and the evolving importance of input features over time. My thesis focuses on addressing these challenges by aiming for more precise explanations of feature interactions, uncovering spatiotemporal patterns, and demonstrating the practical applicability of these interpretability techniques using real-world datasets and state-of-the-art deep learning models. 

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2. Embedding and Trajectories of Temporal Networks

   https://doi.org/10.1109/ACCESS.2023.3268030  

   Thongprayoon, Chanon; Livi, Lorenzo; Masuda, Naoki (January 2023, IEEE Access)
3. Temporal Correlation of Internet Observatories and Outposts

   https://doi.org/10.1109/ipdpsw55747.2022.00054  

   Kepner, Jeremy; Jones, Michael; Andersen, Daniel; Buluc, Aydin; Byun, Chansup; Claffy, K; Davis, Timothy; Arcand, William; Bernays, Jonathan; Bestor, David; et al (May 2022, 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW))

   The Internet has become a critical component of modern civilization requiring scientific exploration akin to endeavors to understand the land, sea, air, and space environments. Understanding the baseline statistical distributions of traffic are essential to the scientific understanding of the Internet. Correlating data from different Internet observatories and outposts can be a useful tool for gaining insights into these distributions. This work compares observed sources from the largest Internet telescope (the CAIDA darknet telescope) with those from a commercial outpost (the GreyNoise honeyfarm). Neither of these locations actively emit Internet traffic and provide distinct observations of unsolicited Internet traffic (primarily botnets and scanners). Newly developed GraphBLAS hyperspace matrices and D4M associative array technologies enable the efficient analysis of these data on significant scales. The CAIDA sources are well approximated by a Zipf-Mandelbrot distribution. Over a 6-month period 70% of the brightest (highest frequency) sources in the CAIDA telescope are consistently detected by coeval observations in the GreyNoise honeyfarm. This overlap drops as the sources dim (reduce frequency) and as the time difference between the observations grows. The probability of seeing a CAIDA source is proportional to the logarithm of the brightness. The temporal correlations are well described by a modified Cauchy distribution. These observations are consistent with a correlated high frequency beam of sources that drifts on a time scale of a month. 

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4. Self-Conscious Emotions and the Right Fronto-Temporal and Right Temporal Parietal Junction

   https://doi.org/10.3390/brainsci12020138  

   LaVarco, Adriana; Ahmad, Nathira; Archer, Qiana; Pardillo, Matthew; Nunez Castaneda, Ray; Minervini, Anthony; Keenan, Julian Paul (February 2022, Brain Sciences)

   For more than two decades, research focusing on both clinical and non-clinical populations has suggested a key role for specific regions in the regulation of self-conscious emotions. It is speculated that both the expression and the interpretation of self-conscious emotions are critical in humans for action planning and response, communication, learning, parenting, and most social encounters. Empathy, Guilt, Jealousy, Shame, and Pride are all categorized as self-conscious emotions, all of which are crucial components to one’s sense of self. There has been an abundance of evidence pointing to the right Fronto-Temporal involvement in the integration of cognitive processes underlying the expression of these emotions. Numerous regions within the right hemisphere have been identified including the right temporal parietal junction (rTPJ), the orbitofrontal cortex (OFC), and the inferior parietal lobule (IPL). In this review, we aim to investigate patient cases, in addition to clinical and non-clinical studies. We also aim to highlight these specific brain regions pivotal to the right hemispheric dominance observed in the neural correlates of such self-conscious emotions and provide the potential role that self-conscious emotions play in evolution. 

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5. Automatically Detecting Arrhythmia-related Irregular Patterns using the Temporal and Spectro-Temporal Textures of ECG Signals

   https://doi.org/10.1109/ICPR.2018.8546045  

   Abdeldayem, Sara S.; Bourlai, Thirimachos (August 2018, International Conference on Pattern Recognition (ICPR))

   Arrhythmia is an abnormal heart rhythm that occurs due to the improper operation of the electrical impulses that coordinate the heartbeats. It is one of the most well-known heart conditions (including coronary artery disease, heart failure etc.) that is experienced by millions of people around the world. While there are several types of arrhythmias, not all of them are dangerous or harmful. However, there are arrhythmias that can often lead to death in minutes (e.g, ventricular fibrillation and ventricular tachycardia) even in young people. Thus, the detection of arrhythmia is critical for stopping and reversing its progression and for increasing longevity and life quality. While a doctor can perform different heart-monitoring tests specific to arrhythmias, the electrocardiogram (ECG) is one of the most common ones used either independently or in combination with other tests (to only detect, e.g. echocardiogram, or trigger arrhythmia and, then, detect, e.g. stress test). We propose a machine learning approach that augments the traditional arrhythmia detection approaches via our automatic arrhythmia classification system. It utilizes the texture of the ECG signal in both the temporal and spectro-temporal domains to detect and classify four types of heartbeats. The original ECG signal is first preprocessed, and then, the R-peaks associated with heartbeat estimation are identified. Next, 1D local binary patterns (LBP) in the temporal domain are utilized, while 2D LBPs and texture-based features extracted by a grayscale co-occurrence matrix (GLCM) are utilized in the spectro-temporal domain using the short-time Fourier transform (STFT) and Morse wavelets. Finally, different classifiers, as well as different ECG lead configurations are examined before we determine our proposed time-frequency SVM model, which obtains a maximum accuracy of 99.81%, sensitivity of 98.17%, and specificity of 99.98% when using a 10 cross-validation on the MIT-BIH database. 

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